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Visually exploring and analyzing event streams / Oracle International Corporation




Visually exploring and analyzing event streams


Some event ordering requirements can be determined based on continuous event processing queries. Other event ordering requirements can be determined based on distribution flow types being used to distribute events from event streams to node executing the queries. Events from event streams can be ordered according to ordering semantics that are based on a combination of all of these event ordering requirements. Additionally, virtual computing nodes can be associated with...



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USPTO Applicaton #: #20170024912
Inventors: Alexandre De Castro Alves, Prabhu Thukkaram, Dmitry Markovski, Ilya Shikalov, Vitaly Bychkov, Natalia Nikiforova


The Patent Description & Claims data below is from USPTO Patent Application 20170024912, Visually exploring and analyzing event streams.


CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a continuation of International Application No. PCT/RU2015/000468, filed Jul. 24, 2015, which application is incorporated herein by reference in its entirety.

BACKGROUND

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Databases have traditionally been used in applications that require storage of data and querying capability on the stored data. Existing databases are thus best equipped to run queries over finite stored data sets. However, the traditional database model is not well suited for a growing number of modern applications in which data is received as a stream of data events instead of a bounded data set. A data stream, also referred to as an event stream, is characterized by a real-time, potentially continuous, sequence of events. A data or event stream thus represents unbounded sets of data. Examples of sources that generate data streams include sensors and probes (e.g., radio frequency identifier (RFID) sensors, temperature sensors, etc.) configured to send a sequence of sensor readings, financial tickers, network monitoring and traffic management applications sending network status updates, click stream analysis tools, and others.

Continuous event processing (CEP) is a technology useful for processing data in an event stream. CEP is highly stateful. CEP involves receiving events continuously, and finding some pattern among those events. A significant amount of state maintenance is therefore involved in CEP. Because CEP involves the maintenance of so much state, processes which apply CEP queries to data within an event stream have always been single-threaded. In computer programming, single-threading is the processing of one command at a time.

CEP query processing generally involves the continuous execution of a query relative to events that are specified within an event stream. For example, CEP query processing might be used in order to continuously observe the average price of a stock over the most recent hour. Under such circumstances, CEP query processing can be performed relative to an event stream that contained events that each indicated the current price of the stock at various times. The query can aggregate the stock prices over the last hour and then calculate the average of those stock prices. The query can output each calculated average. As the hour-long window of prices moves, the query can be executed continuously, and the query can output various different average stock prices.

A continuous event processor is capable of receiving a continuous stream of events and processing each event contained therein by applying a CEP query to that event. Such a CEP query may be formatted in conformance to the syntax of a CEP query language such as the continuous query language (CQL), which is an extension of the structured query language (SQL). Whereas SQL queries are often applied once (per user request) to data that has already been stored in the tables of a relational database, CQL queries are applied repeatedly to events in an incoming event stream as those events are received by the continuous event processor.

BRIEF

SUMMARY

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Embodiments described herein relate to databases and continuous event processing. According to some embodiments, the processing of CQL queries can be distributed across disparate processing nodes. An event processing mechanism can be distributed across multiple separate virtual machines.

According to some embodiments, an HBase database store is used as data source for a CQL processor. This use allows events to be enriched by data that exists in this store, similar to how events can be enriched with data that exists in a RDBMS table. According to some embodiments, an HBase database store is used as a data sink similar to a table sink feature.

The foregoing, together with other features and embodiments will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

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FIG. 1 is a diagram that illustrates an example of a table in an HBase data store, according to some embodiments.

FIG. 2 is a block diagram that illustrates an example of a simple event processing network, according to some embodiments.

FIG. 3 is a block diagram that illustrates an example of a broadcast event processing network, according to some embodiments.

FIG. 4 is a block diagram that illustrates an example of a load-balancing event processing network, according to some embodiments.

FIG. 5 is a block diagram that illustrates an example of a subsequent state of a load-balancing event processing network, according to some embodiments.

FIG. 6 is a block diagram that illustrates an example of a broadcast event processing network in which a channel has two consumers, according to some embodiments.

FIG. 7 is a flow diagram that illustrates an example of a technique for generating a single token usable to request services from multiple resource servers, according to an embodiment of the invention.

FIG. 8 is a block diagram that illustrates an example of a partitioned event processing network, according to some embodiments.

FIG. 9 is a block diagram that illustrates another example of a partitioned event processing network, according to some embodiments.

FIG. 10 is a block diagram that illustrates an example of a fan-in event processing network, according to some embodiments.

FIG. 11 is a diagram that illustrates an example of a line graph, according to some embodiments.

FIG. 12 is a diagram that illustrates an example of a scatter plot, according to some embodiments.

FIG. 13 is a diagram that illustrates an example of a scatterplot in which a smoothed curve fitter has been drawn, according to some embodiments.

FIG. 14 is a diagram that illustrates an example of a scatterplot in which the points are differently sized, according to some embodiments.

FIG. 15 is a diagram that illustrates an example of a radar plot, according to some embodiments.

FIG. 16 depicts a simplified diagram of a distributed system for implementing one of the embodiments.

FIG. 17 is a simplified block diagram of components of a system environment by which services provided by the components of an embodiment system may be offered as cloud services, in accordance with an embodiment of the present disclosure.

FIG. 18 illustrates an example of a computer system in which various embodiments of the present invention may be implemented.

FIG. 19 is a diagram that illustrates an example of cluster-representing shapes being overlaid on a scatter plot, according to some embodiments.




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stats Patent Info
Application #
US 20170024912 A1
Publish Date
01/26/2017
Document #
14866512
File Date
09/25/2015
USPTO Class
Other USPTO Classes
International Class
/
Drawings
20


Latency Ordering Semantic Semantics

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20170126|20170024912|visually exploring and analyzing event streams|Some event ordering requirements can be determined based on continuous event processing queries. Other event ordering requirements can be determined based on distribution flow types being used to distribute events from event streams to node executing the queries. Events from event streams can be ordered according to ordering semantics that |Oracle-International-Corporation
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